Accurate estimation of battery states such as State of Charge (SOC) and State of Health (SOH) is essential for the performance and safety of battery systems, especially in electric vehicles and renewable energy storage. Traditional methods like Kalman Filter (KF), Extended Kalman Filter (EKF), and Particle Filter (PF) offer useful solutions but face challenges with nonlinearity, sensor noise, and long-term accuracy. Factor Graph Optimization (FGO) has recently emerged as a robust alternative, integrating multiple data sources with prior knowledge in a probabilistic framework. This paper reviews state estimation methods with a focus on FGO, comparing it to conventional techniques in terms of accuracy, robustness, and computational cost. A simulation framework built with MATLAB Simscape Battery evaluates their performance under varying conditions. We also discuss the integration of FGO with data-driven models and its potential for real-time deployment.